arXiv (All)
open access
TurboMamba: Faster Inference, Faster Training, and Better Prediction with Lexical Memory
We introduce TurboMamba, a language-model architecture for faster inference, faster training, and better token prediction. A global Mamba-3 model runs once per completed token block. A small Transformer uses this global context and earlier tokens within the block to predict the next token. Three lig
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Counting and Min-Cost Encoding for Tokenization in Large Language Models#1156062
- Training LLMs Beyond Next Token Prediction - Filling the Mutual Information Gap#1342357
- TopoCompress: Long Context Compression via Graph-Wired Semantic Trajectories#820699
- AdaptInfer: Adaptive Token Pruning for Vision-Language Model Inference via Dynamical Text Guidance#1200775
- Advantageous Parameter Expansion Training Makes Better Large Language Models#668277
Record · ID 1355671
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive